Field
Aspects of the embodiments described relate generally to data mining and data correlation analysis. More particularly, the embodiments relate to integrating manual and automated techniques for data mining and data correlation analysis of industrial process data.
Description of the Related Art
Multivariate models can be used to detect when industrial processes are operating in an acceptable condition or a fault condition. Multivariate models enable operators of process-controlled equipment to monitor a relatively small number of metrics when compared to what can sometimes be an overwhelming number of data points monitored by a control system.
Multivariate models for a specific tool are often developed after a training period during which processes for the tool are repeated under controlled conditions and vast amounts of data, including faults, are logged. The most relevant data is used to develop models for different portions of the processes. Then the models are tested to determine if the models accurately predict when the tool is operating in an acceptable condition or a fault condition. Once the models prove satisfactory, fault thresholds can be set, and the models can be deployed for use in production.
Multivariate models can also be used to detect faults that could not be detected by only one sensor or would at least be more challenging to detect. For example, using measurements from a specific pressure sensor and a specific flow meter, a system monitoring a multivariate model may be able to detect with a very high confidence that a specific valve is presenting a significant risk of failing in the near future. Such confidence can be obtained because the multivariate model of the pressure sensor and flow meter could show repeatable and identifiable patterns when that specific valve needs a specific type of maintenance, while these patterns could not be detected using univariate models. These identifiable patterns can be used in predictive maintenance (PdM) allowing a plant to perform maintenance at the most opportune times as opposed to the traditional maintenance schedules applied in preventive maintenance.
If the valve fault described above is recognized during the training period, then a multivariate model developer can include the fault as one of the multivariate faults that the system will monitor. If the valve fault is not recognized during the training period, then an operator may struggle to determine the problem despite noticing that a trend or value from one of the multivariate models is abnormal.
When faced with an abnormal pressure and flow signal, it may be obvious that a valve could be the issue. When faced with a somewhat abnormal set of values from a multivariate model, that could include dozens of inputs, the solution is not so obvious.
Fully automated data analysis systems cannot always be developed to detect every potential fault or issue that could occur on a piece of equipment. Similarly, strictly manually directed techniques for data analysis, such as computer routines for summarizing data, are too cumbersome to be cost effective in large data analysis problems. The shortfalls of fully automated data analysis systems and strictly manual techniques are especially true in attempts to determine what data or parameters are important in a correlation analysis exercise.
Therefore, a need exists for an improved monitoring system that enables an operator to quickly determine the possible causes of abnormalities in sets of values from multivariate models.